On Fitting a Multivariate Two-Part Latent Growth Model

Shu Xu; Shelley A. Blozis; Elizabeth A. Vandewater

doi:10.1080/10705511.2014.856699

On Fitting a Multivariate Two-Part Latent Growth Model

Shu Xu, Shelley A. Blozis, Elizabeth A. Vandewater

Global Public Health

Research output: Contribution to journal › Article › peer-review

Abstract

A 2-part latent growth model can be used to analyze semicontinuous data to simultaneously study change in the probability that an individual engages in a behavior, and if engaged, change in the behavior. This article uses a Monte Carlo (MC) integration algorithm to study the interrelationships between the growth factors of 2 variables measured longitudinally where each variable can follow a 2-part latent growth model. A SAS macro implementing Mplus is developed to estimate the model to take into account the sampling uncertainty of this simulation-based computational approach. A sample of time-use data is used to show how maximum likelihood estimates can be obtained using a rectangular numerical integration method and an MC integration method.

Original language	English (US)
Pages (from-to)	131-148
Number of pages	18
Journal	Structural Equation Modeling
Volume	21
Issue number	1
DOIs	https://doi.org/10.1080/10705511.2014.856699
State	Published - Jan 2014

Keywords

Monte Carlo integration
longitudinal semicontinuous variables
multivariate two-part latent growth curve model

ASJC Scopus subject areas

General Decision Sciences
Modeling and Simulation
Sociology and Political Science
Economics, Econometrics and Finance(all)

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10.1080/10705511.2014.856699

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abstract = "A 2-part latent growth model can be used to analyze semicontinuous data to simultaneously study change in the probability that an individual engages in a behavior, and if engaged, change in the behavior. This article uses a Monte Carlo (MC) integration algorithm to study the interrelationships between the growth factors of 2 variables measured longitudinally where each variable can follow a 2-part latent growth model. A SAS macro implementing Mplus is developed to estimate the model to take into account the sampling uncertainty of this simulation-based computational approach. A sample of time-use data is used to show how maximum likelihood estimates can be obtained using a rectangular numerical integration method and an MC integration method.",

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On Fitting a Multivariate Two-Part Latent Growth Model

Abstract

Keywords

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